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Purpose. As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively. Conclusion. The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other. [ABSTRACT FROM AUTHOR]

In this paper, an integrated workflow based on recent geoscience data is presented for assessing the reservoir characterization and structural interpretation of the Burgan formation, a highly productive formation in the Abadan plain, SW Iran. Utilizing newly acquired high-resolution SEM images, we evaluated the pore size, pore distribution, and pore aspect ratio of Burgan formation. The watershed segmentation algorithm is also capable of detecting throats and closed pores. The porosity fractions from SEM images are used for calibration of the porosity log at several well locations in order to perform petrophysical modeling. Due to the complexity of facies behavior in the study area, we utilized supervised Bayesian classifiers using P-wave velocity, density, and facies log data in order to incorporate spatial dependence into the model. The confusing matrix and machine learning metrics including Accuracy (97.01%), Precision (93.88%), F1 Score (94.16%), and False Positive Rate (2.52%), indicate that the classifier has been properly trained at well locations. A reasonable match is evident between the modeled petrophysical parameters and the true (core) porosity and water saturation at the location of the test well. Furthermore, we have demonstrated the validity of assumptions concerning the dominance of extensional structure in the Abadan plain by using interpreted seismic data. The presented workflow can be used to optimize drilling operations and reduce risks in similar geological settings in the studied formation. Highlights: • The vital pore properties have been evaluated using newly obtained SEM images. • Bayesian classification is a reliable classifier for complex facies modeling. • The dominant extensional structure can be observed in the Abadan plain. • The watershed segmentation algorithm can detect closed pores and throats. [ABSTRACT FROM AUTHOR]

Different geophysical methods use different model parameterizations and inversion algorithms. Thus, combining these different inversion systems and yet adding the nonlinear cross‐gradient constraint in a joint inversion framework might be a big challenge, for instance, as explained further by Moorkamp et al. in 2011, there is a complex interaction between the data misfit terms, regularization and cross‐gradient terms and an imperfect fit to the data is expected. In this paper, we use a sequential algorithm for a two‐dimensional joint inversion of gravity and magnetic data, which tries to avoid these issues by decoupling the gravity inversion, the magnetic inversion and the cross‐gradient minimization processes. The efficiency of the algorithm and developed code is demonstrated by the joint inversion of noisy synthetic data. The results show a significant improvement in the respective models obtained by introducing the cross‐gradient joint inversion over the models obtained by separate inversions for synthetic data and then for field data targeting potash ore source in the AjiChai salt deposit in north‐western Iran. In this application case, the lower density of salt minerals such as potash, compared to its surrounding sedimentary sequences, motivates a gravity study. Furthermore, the relative lower susceptibility of these salt minerals, alongside their diamagnetic effect, makes them a suitable target for magnetic surveys. Separate gravity and magnetic studies had been performed over the deposit; however, a constitutive relationship between density and magnetization within the area of interest supporting a joint inversion had not been established. In this paper, we apply the sequential cross‐gradient approach to perform the first full joint inversion for the AjiChai salt deposit. The magnetic inversion here is performed to recover the magnetization amplitudes rather than the magnetization vector. In fact, we assume there is no remanent magnetization and, therefore, that the magnetization vector is constant and parallel to the geomagnetic field direction. The constructed density and magnetization models are of high concordance with available geological information and previous studies including drilling results. In addition, unlike previous separate inversion models, the models are structurally and geometrically similar. [ABSTRACT FROM AUTHOR]

Planning and scheduling of operating rooms (ORs) is important for hospitals to improve efficiency and achieve high quality of service. Due to significant uncertainty in surgery durations, scheduling of ORs can be very challenging. In this paper, surgical case scheduling problem with uncertain duration of surgeries in multi resource environment is investigated. We present a two-stage stochastic mixed-integer programming model, named SOS, with the objective of total ORs idle time and overtime. Also, in this paper a two-step approach is proposed for solving the model based on the L-shaped algorithm. Proposing the model in a multi resources environment with considering real-life limitations in academic hospitals and developing an approach for solving this stochastic model efficiently form the main contributions of this paper. The model is evaluated through several numerical experiments based on real data from Hasheminejad Kidney Center (HKC) in Iran. The solutions of SOS are compared with the deterministic solutions in several real instances. Numerical results show that SOS enjoys a better performance in 97% of the cases. Furthermore, the results of comparing with actual schedules applied in HKC reveal a notable reduction of OR idle time and over time which illustrate the efficiency of the proposed model in practice. [ABSTRACT FROM AUTHOR]

Summary: The purpose of this paper is to present a model for optimal placement of distributed generation units and sectionalizing switches (relocation of the existing switches and installation of new ones) to increase distribution network resilience against the threat of transmission system outage. The proposed model is formulated as a multi‐objective optimization problem with the objectives of minimizing investment costs and maximizing load restoration. In this regard, distribution system operation during normal conditions as well as transmission system outages is considered. In addition, the unique characteristics of the real power grid in Khuzestan province in southwestern of Iran are addressed in the proposed model which makes the model applicable to the real networks with similar characteristics in many parts of the Middle East. This grid has two completely different load patterns (in terms of load curve shape and peak load) corresponding to two hot and temperate periods, which causes completely different concerns for the network's operator. The proposed model is solved using multi‐objective genetic algorithm (NSGA‐II), and it is implemented on the 33‐node test system and the results are analyzed. Moreover, it is applied to the 327‐node real‐world test system in Khuzestan province to demonstrate its applicability to large networks. In both cases, the results confirm the effectiveness of the proposed optimization model. [ABSTRACT FROM AUTHOR]

The Cauchy problem for Helmholtz equation, for moderate wave number |$k^{2}$|⁠ , is considered. In the previous paper of Achieng et al. (2020, Analysis of Dirichlet–Robin iterations for solving the Cauchy problem for elliptic equations. Bull. Iran. Math. Soc.), a proof of convergence for the Dirichlet–Robin alternating algorithm was given for general elliptic operators of second order, provided that appropriate Robin parameters were used. Also, it has been noted that the rate of convergence for the alternating iterative algorithm is quite slow. Thus, we reformulate the Cauchy problem as an operator equation and implement iterative methods based on Krylov subspaces. The aim is to achieve faster convergence. In particular, we consider the Landweber method, the conjugate gradient method and the generalized minimal residual method. The numerical results show that all the methods work well. In this work, we discuss also how one can approach non-symmetric differential operators by using similar operator equations and model problems which are used for symmetric differential operators. [ABSTRACT FROM AUTHOR]

In the real word, the decision maker may want to use revenue and cost eficiency analysis for existing units compared to virtual units, traditional DEA models can no longer be used and FDH models must be used to evaluate the efficiency of decision-making units. In this paper, we develop the revenue and cost efficiency models based on the FDH model and obtain the efficiency scores based on pair wise comparisons. In the following, we will develop the proposed models for the two-stage network structure and obtain the desired scores of inputs and outputs by considering the input and output prices. An algorithm for measuring the revenue and cost efficiency is presented based on the ratio of inputs and outputs. Finally, we use the proposed algorithm to evaluate the efficiency of 13 airports in Iran with a two-stage network structure without considering the constraint of convexity and present the results of the research. [ABSTRACT FROM AUTHOR]

Improving network power quality through harmonic reduction requires recognition of Harmonic Sources (HSs) to drive them to compensate their harmonics. This paper proposes a new method for equitable distribution of the Harmonic Compensation Cost of the network among the HSs based on the Harmonic Contribution Matrix. Each element of the Harmonic Contribution Matrix is the harmonic contribution of a specific source to the harmonic voltage of a specific bus. The output of the proposed method is a penalty curve for each HS over time. The amount of the fine estimated for each individual HS per hour is a function of not only the contribution of that HS to the harmonic voltage of different buses, but also the contribution of the Harmonic Compensation Cost from the perspective of each bus, nominal voltage of each bus, and the sensitivity of each bus to the harmonic voltage. The proposed algorithm is evaluated on the IEEE 14‐bus network and Esfahan regional electrical power network in Iran. The simulation results demonstrate the capability of the proposed method to allocate the hourly penalty curve to the HSs. [ABSTRACT FROM AUTHOR]

In ophthalmologic or otolaryngologic studies, bilateral correlated data often arise when observations involving paired organs (e.g., eyes, ears) are measured from each subject. Based on Donner's model , in this paper, we focus on investigating the relationship between the disease probability and covariates (such as ages, weights, gender, and so on) via the logistic regression for the analysis of bilateral correlated data. We first propose a new minorization–maximization (MM) algorithm and a fast quadratic lower bound (QLB) algorithm to calculate the maximum likelihood estimates of the vector of regression coefficients, and then develop three large-sample tests (i.e., the likelihood ratio test, Wald test, and score test) to test if covariates have a significant impact on the disease probability. Simulation studies are conducted to evaluate the performance of the proposed fast QLB algorithm and three testing methods. A real ophthalmologic data set in Iran is used to illustrate the proposed methods. [ABSTRACT FROM AUTHOR]

This paper presents an improved nature-based algorithm, namely multivariable modified teaching learning based optimization (MM-TLBO) algorithm, as in an iterative process can estimates the best values for the model parameters in a multi-objective problem. The algorithm works in two computational phases: the teacher phase and the learner phase. The major purpose of the MM-TLBO algorithm is to improve the value of the learners and thus, improving the value of the model parameters which leads to the optimal solution. The variables of each learner (model) are the radius (R), depth (h), shape factor (q), density contrast (q) and axis location (x0) parameters. We apply MM-TLBO and TLBO methods for the residual gravity anomalies caused by the buried masses with a simple geometry such as spheres, horizontal and vertical cylinders. The efficiency of these methods are also tested by noise corruption synthetic data, as the acceptable results were obtained. The obtained results indicate the better performance the MM-TLBO algorithm than the TLBO algorithm. We have utilized the MM-TLBO for the interpretation of the six residual gravity anomaly profiles from Iran, USA, Sweden and Senegal. The advantage of the MM-TLBO inversion is that it can estimates the best solutions very fast without falling into local minimum and reaches to a premature convergence. The considered primary population for the synthetic and real gravity data are thirty and fifty models. The results show which this method is able to achieve the optimal responses even if a small population of learners had been considered. [ABSTRACT FROM AUTHOR]

Grouping of earthquakes into distinct clusters is applied to improve mechanism identification and pattern recognition for active seismicity in a region. One of the important issues concerning earthquake data clustering is determining the optimum number of clusters (ONC) at the early stages of algorithms. In this paper a robust method based on K-nearest neighbor search (KNNS) is presented to achieve three goals: improving output accuracy, improving output stability, and adding the ability to weight the features used in ONC determination. By introducing a new formula, the proposed method utilizes the error calculated for clustered data based on the similarity between the members in each cluster. An outlier attenuation algorithm is also used to improve the performance of the method. Both the Krzanowski–Lai Index (KLI) and the silhouette coefficient (SC), as two conventional methods, were used to compare the results and evaluate the performance. Experiments on synthetic data sets verified the effectiveness of the method, with considerable differences found. The clustering of a real earthquake catalogue related to the seismogenic province of Zagros in Persia using our proposed methodology suggests using 13-cluster analysis for clustering based on the spatiotemporal features with the same weights, and seven-cluster analysis for a case where priority is given only to the spatial parameters of the epicenters. Under the same circumstances, the KLI and SC methods suggest three and 18 clusters, respectively. The results of the experiments on synthetic data sets indicate that the proposed method is quantitatively more stable and more accurate than the other two methods. [ABSTRACT FROM AUTHOR]

As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic crash severity based on artificial neural networks (ANNs), support vector machines and decision trees. This paper attempts to investigate the crash injury severity of rural roads by using a hybrid clustering and classification approach to compare the performance of classification algorithms before and after applying the clustering. In this paper, a novel rule-based genetic algorithm (GA) is proposed to predict crash injury severity, which is evaluated by performance criteria in comparison with classification algorithms like ANN. The results obtained from analysis of 13,673 crashes (5600 property damage, 778 fatal crashes, 4690 slight injuries and 2605 severe injuries) on rural roads in Tehran Province of Iran during 2011–2013 revealed that the proposed GA method outperforms other classification algorithms based on classification metrics like precision (86%), recall (88%) and accuracy (87%). Moreover, the proposed GA method has the highest level of interpretation, is easy to understand and provides feedback to analysts. [ABSTRACT FROM PUBLISHER]

This paper considers a real case problem of supply chain network design inspired from a wheat distribution network in Iran. It generates a network with capacity acquisition and fleet management. The problem first is formulated as a mixed integer linear programming model. Then, a logic-based Benders decomposition algorithm is appropriately developed as the solution methodology. In the presented algorithm, the problem is decomposed into two models of master and subproblem. The master problem is improved by means of the preprocessing and valid inequalities. Moreover, three Benders cuts, one optimality and two feasibility cuts, are developed for the algorithm. The general and relative performance of the model and algorithm is experimentally evaluated. The wheat distribution system of Iran is considered here as the case study of this research. The model is developed based on Iran's wheat distribution system. All the results show that the algorithm significantly outperforms the mathematical model of the case study. For example, the algorithm solves 95% of the tested instances to optimality, yet the model solves 29%. [ABSTRACT FROM AUTHOR]

This paper presents a nature-based algorithm, titled multivariable teaching-learning-based optimization (MTLBO) algorithm. MTLBO algorithm during an iterative process can estimates the best values of the buried structure (model) parameters in a multi-objective problem. The algorithm works in two computa-tional phases: the teacher phase and the learner phase. The major purpose of the MTLBO algorithm is to modify the value of the learners and thus, improving the value of the model parameters which leads to the optimal solution. The variables of each learner (model) are the depth (z), amplitude coefficient (k), shape factor (q), angle of effective magnetization (Î,) and axis location (x0) parameters. We employ MTLBO method for the magnetic anomalies caused by the buried structures with a simple geometric shape such as sphere and horizontal cylinder. The efficiency of the MTLBO is also studied by noise corruption synthetic data, as the acceptable results were obtained. We have applied the MTLBO for the interpretation of the four magnetic anomaly profiles from Iran, Brazil and India. [ABSTRACT FROM AUTHOR]

In this paper optimal designing of two hybrid photovoltaic/wind turbine (PV/WT) systems with different storage include battery and hydrogen is presented with objective of minimising cost of energy generation (COEG) and considering reliability constraint as deficit power probability of the system using improved salp swarm optimisation algorithm (ISSOA) for commercial centre area, for Iran country. Different hybrid systems are compared in view of cost and reliability and the optimal size is determined. The results show that the PV/WT with battery storage has lower COEG than PV/WT with hydrogen storage to supply the commercial centre and also its reliability is better than the system integrated with hydrogen. The average cost of per kW of load supply for the PV/WT system with battery and hydrogen storage is 0.216$ and 0.610$, respectively. Superiority of ISSOA is confirmed in comparison with conventional SSOA and particle swarm optimisation in hybrid systems designing. [ABSTRACT FROM AUTHOR]

The special features of some old residential complexes make reconstructing a better option than rebuilding. The complexity of redesign issues, the ability of computers to solve problems, and the capability of evolutionary optimization techniques in architecture make the use of these methods a priority over traditional processes. This paper, while introducing Particle Swarm Optimization (PSO) as one of the swarm intelligence based algorithms, answers the question of how this algorithm can be used to redesign residential complexes. To achieve this, after reviewing the application of PSO algorithms in architectural space planning, the redesign criteria and constraints was divided into four categories; including primary design specifications, contact-related criteria, designer-related criteria and redesign-related constraints. After the quantification process of these criteria, how to implement the algorithm to achieve optimal plans for each of the five plan types available in high-rise apartment complex in Mashhad, Iran, was shown as a case study. [ABSTRACT FROM AUTHOR]

We will refer to the paper "Convergence rates for Kaczmarz-type algorithms", published in Numerical Algorithms, 79(1)(2018), 1-17. It was observed by Dr. Mokhtar Abbasi (Department of Mathematics, University of Qom, Iran) that in the proof of Theorem 7, related to the linear convergence rate of the MREK algorithm it exists an inequality which is not always true. [ABSTRACT FROM AUTHOR]

This paper couples an adaptive neuro-fuzzy inference system (ANFIS), with two heuristic-based computation methods namely biogeography-based optimization (BBO) and BAT algorithm (BA) with GIS to map flood susceptibility in a region of Iran. These algorithms have been used for flood modelling, infrequently. A total of 287 flood locations were randomly categorized into training (70%; 201 floods), and validation (30%; 86 floods) datasets for modelling process and evaluation. The Step-wise Weight Assessment Ratio Analysis (SWARA) technique was applied to evaluate the role of nine dominant factors on flood occurrence. The results of using the ANFIS and the artificial intelligence ensemble algorithms were three flood susceptibility maps. Results indicated that the ANFIS-BBO had the highest accuracy in comparison with the ANFIS and ANFIS-BA models in flood modelling. In addition, BBO algorithm showed its great potential by considering higher accuracy and lower computational time, in mapping and assessment of flood susceptibility. [ABSTRACT FROM AUTHOR]

Abstract: Natural language processing is a composition of several error‐prone and challenging tasks, including part of speech tagging, word sense disambiguation, named entity recognition, and compound verb detection. Studying intrasentence relations and roles is essential to improve the mentioned subtasks. Semi‐automatic schemes such as ontologies can be applied to clarify word's dependencies. This paper presents an ontology that is targeting to improve POS tagging, WSD, NER, and compound verb detection in Persian with extra properties that may ameliorate machine translation. The ontology is tested in combinations with several state‐of‐art algorithms on Dadegan corpus. The results show that coping semantic analysis with machine learning methods enhance relation detection and consequently precision of the mentioned subtasks, which is not widely addressed in Persian. Furthermore, the experimental results declare that the accuracy rate increases between 4.5 and 23% for different tasks. [ABSTRACT FROM AUTHOR]

• A new method based on the discrete energy separation algorithm presents to HIF and LIF detection in distribution system. • Simple implementation and low computational burden are main advantages of the method. • The method detects HIF and LIF from other disturbances with high detection accuracy and low detection time even in noisy condition. • The method has proper performance in detection of recorded real HIFs in distribution system. • CT saturation has no impact on the detection accuracy and time of the method in LIF detection. Due to the low fault current of high impedance fault (HIF), it is not recognized by overcurrent relays. Moreover, due to the similar characteristics of HIF and other disturbances like capacitor bank (CB) and non-linear load (NL) switching, detection of HIF is one of the main challenges in protection of distribution system. Furthermore, detection of low impedance fault (LIF) is important for protection system. In this paper, a new statistical method is proposed based on a discrete time separation algorithm of energy for detection of HIF and LIF from other disturbances to improve detection accuracy and time. The method consists of two main parts, the first one detects switching on/off the linear load, switching off the CB and switching off the NL from abnormal conditions, while the second part identifies the HIF and LIF from switching on the CB and switching on the NL. High detection speed, simple implementation, and low computational burden are advantages of the method. The method is tested on IEEE 13-node and IEEE 34-node distribution test systems, and a real feeder in Iran. Obtained results show that the high efficiency of the proposed method in detection of disturbances even in noisy conditions, with low detection time. [ABSTRACT FROM AUTHOR]

Daily increasing demand of electrical energy and also recent developments in technology of distributed generations (DGs) have created special interest in using these units. On the other hand, capacitor insertion in the distribution feeders has been implemented by distribution companies for several years to reduce power loss and improve the voltage profile. Hence, according to the joint role of distributed generation units and capacitors, their optimal allocation will be influenced by each other. Therefore, in order to achieve the maximum benefits, this paper has discussed the optimal siting and sizing of DG resources and capacitors. One of the characteristics of the proposed method is that all technical and economical factors associated with installation of DGs and capacitors will be priced. For this purpose, all effective factors in optimal allocation and sizing of DG resources and capacitors are extracted and priced. For example, for the first time, the factor of voltage profile has been priced by the number of tapping. Using this approach, weighting the objective function parameters would be no more required. One of the other features of this paper is presenting an algorithm to determine the optimum number of DGs and capacitors. With this algorithm, power companies can find the optimum number of DGs and capacitors in the network they cover. The proposed method in this paper is tested on the 97-bus network of Bakhtar Regional Electrical Company in Iran Country. [ABSTRACT FROM AUTHOR]

Mid-term load forecasting (MTLF) becomes an essential tool for today power systems, mainly in those countries whose power systems operate in a deregulated environment. Among different kinds of MTLF, this paper focuses on the prediction of daily peak load for one month ahead. This kind of load forecast has many applications like maintenance scheduling, mid-term hydro thermal coordination, adequacy assessment, management of limited energy units, negotiation of forward contracts, and development of cost efficient fuel purchasing strategies. However, daily peak load is a nonlinear, volatile, and nonstationary signal. Besides, lack of sufficient data usually further complicates this problem. The paper proposes a new methodology to solve it, composed of an efficient data model, preforecast mechanism and combination of neural network and evolutionary algorithm as the hybrid forecast technique. The proposed methodology is examined on the EUropean Network on Intelligent TEchnologies (EUNITE) test data and Iran’s power system. We will also compare our strategy with the other MTLF methods revealing its capability to solve this load forecast problem. [Copyright &y& Elsevier]

This paper presents a robust device for automated screening of pediatric heart diseases based on our unique processing method in murmur characterization; the Arash-Band method. The present study modifies the Arash-Band method and employs output of the modified method in conjunction with the two other original techniques to extract indicative feature vectors for the screening. The extracted feature vectors are classified by using the support vector machine method. Results show that the proposed modifications significantly enhances performance of the Arash-Band in terms of the both accuracy and sensitivity as the corresponding effect sizes are sufficiently large. The proposed algorithm has been incorporated into an Android-based tablet to constitute an intelligent phonocardiogram with the automatic screening capability. In order to obtain confidence interval of the accuracy and sensitivity, an inferable statistical test is applied on our database containing the phonocardiogram signals recorded from 263 of the referrals to a hospital. The expected value of the accuracy/sensitivity is estimated to be 87.45 % / 87.29 % with a 95 % confidence interval of (80.19 % - 92.47 %) / (76.01 % - 95.78 %) exhibiting superior performance than a pediatric cardiologist who relies on conventional or even computer-assisted auscultation. [ABSTRACT FROM AUTHOR]

In this paper, an optimization model is developed to determine the best size of a stand-alone hybrid renewable energy system (HRES) for electrification to a remote area located in Kerman, Iran. The model is defined based on three decision variables related to the system components, namely, total area occupied by the set of PV panels (a continuous variable), total swept area by the rotating turbines’ blades (a continuous variable) and the number of batteries (an integer variable). In order to find the optimal values of the variables, particle swarm optimization (PSO) and some of its variants are proposed. Due to the non-linearity and non-convexity of the sizing problem, PSO which is an efficient population-based heuristic technique can be a good candidate. Particles of PSO probe the search space to minimize the life cycle cost (LCC), ensuring at the same time certain level of system reliability. Simulation results reveal that the PV/WT/battery system is the most cost-effective one and adaptive inertia weight-based PSO algorithm yields more promising results than the other PSO variants. [ABSTRACT FROM AUTHOR]

Hub location problem is further used in transportation and telecommunication networks (airlines, post delivery services, etc.) so origin-destination pairs, receive or send commodities via special facilities called Hub. Hub median problem with multiple allocation is an NP-hard problem which includes both locating hub facilities and allocating non-hub nodes to hubs as minimizes total transportation and location costs. In this paper, the hub median problem is considered in an environment which network flow varies during the time periods and the capacities of hubs and arcs are unlimited. Also opening and closing hubs in different periods of planning horizon are permitted. The model and the proposed algorithm for this problem were considered to Iran airlines network using real passenger flows data. Computational results state that the dynamic network compared with static model has lower cost and whatever the number of time periods in dynamic case increases, the cost will be reduced as well. [ABSTRACT FROM AUTHOR]

Abstract: Various studies have been conducted thus far to classify the seismicity regions of Iran into zones that share similar seismic characteristics. In these studies, seismotectonic models have been presented that implement various information layers based mostly on human expert judgment. The purpose of the present research is to develop a seismotectonic model using all of the available information about the layers along with an automated smart clustering algorithm. In this paper, based on a review of past research studies, a combined model is presented, which involves the combination of the four most-referenced seismotectonic models. To build upon past studies, a smart algorithm called the self organizing map (SOM) was selected for clustering the seismotectonic data of provinces. Input layers, such as earthquake coordinates, seismicity, faults distribution, topography, gravity, and magnetism, were used to execute the SOM smart algorithm. Based upon previous studies, the number of clusters was selected in the range of 5–25. Three of the SOM algorithm outputs, which were more compatible with the combined model, were selected for use in this study. According to the clustering validation methods and expert judgments, the highest compatibility belongs to the models that were produced either by all of the information or by the combination of seismicity, faults, and topography layers. Implementation of the clustering validation methods indicated that the optimum number of seismotectonic zones is between 9 and 13. This study emphasizes that the application of smart methods along with various geological, structural, subsurface, and aerial information layers will produce informative and reliable results. [Copyright &y& Elsevier]

Seasonal demand variation is a somewhat neglected aspect of transit demand. Many cities worldwide experience seasonal variation of passenger demand that deserves not only changes of service frequency, but also and often more importantly, changes in the transit network of routes. Literature dealing with optimal bus networks shows that it is an non polynomial (NP)-hard complexity problem with the demand being the most important variable affecting the network. Thus, the bus network of concern should be robust from the optimization perspective when a new demand arises. This paper introduces a model and a solution algorithm to take into account the variation of passenger demand in the design of bus networks. The solution algorithm developed is a hybrid method that optimizes the design of a bus network at the route and network levels. The model was applied to the city of Mashhad in Iran with a population of over 3.2 million and 20 million visitors annually. The results are promising and demonstrate how to determine the best single network of bus routes to suit variable passenger demand. [ABSTRACT FROM AUTHOR]

The railroad blocking problem (RBP) is an important decision for freight railroad companies. The objective of this problem is to minimize the costs of delivering all commodities by deciding which interyard blocks to build and specifying the assignment of commodities to these blocks. In this paper, a mathematical model is presented for the RBP on Iran Railways. Its decision variables identify blocking scheme and demand assignment to these blocks. The RBP in medium and large sizes is not solvable with any commercial software available in the market. Therefore, a solution method based on tabu search algorithm is proposed for the suggested model. For evaluating the proposed algorithm, several simulated test problems are randomly generated and solved. The obtained results on the test problems are compared with those of solutions generated by CPLEX software. The comparison shows high efficiency and effectiveness of the proposed algorithm. The proposed model and solution method are applied to build a blocking plan for the Iranian railway. [ABSTRACT FROM AUTHOR]

In this paper a new meta-heuristic approach based on the nature of honey bees mating has been used for the calibration of a real city in the north of Iran named Langarud. This city has a population of nearly 68,000 people and about 43,000 water consumers. Langarud's area is about 900 km². The method was used to determine the Hazen-Williams roughness factor of the main pipes of the town and a correction factor for the nodal demands in the main nodes of the network. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present a new tool for design of integrated energy systems. The initial choice of the energy system components and the way they should interact is a crucial decision which the outcome of the design heavily relies on. Use of a physical law (instead of engineering judgment) as the basis of the decision making is the main advantage of the proposed approach over conventional approaches for design of community scale energy systems. The methodology has been implemented for design of a district heating system for an existing district in arid region of Iran. The optimum level of interaction between the energy system components has been identified by employing an optimization algorithm seeking to minimize the overall cost of the energy system. Some of the relative merits of the optimum design comparing to the present energy system are 17% increase of the exergy efficiency, 10.8% reduction in the amount of CO2 production per capita and 2% reduction in overall energy related costs. [Copyright &y& Elsevier]

This paper is an attempt to introduce the role of earth observation technology and a type of digital earth processing in mineral resources exploration and assessment. The sub-pixel distribution and quantity of alteration minerals were mapped using linear spectral unmixing (LSU) and mixture tuned matched filtering (MTMF) algorithms in the Sarduiyeh area, SE Kerman, Iran, using the visible-near infrared (VNIR) and short wave infrared (SWIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument and the results were compared to evaluate the efficiency of methods. Three groups of alteration minerals were identified: (1) pyrophylite-alunite (2) sericite-kaolinite, and (3) chlorite-calcite-epidote. Results showed that high abundances within pixels were successfully corresponded to the alteration zones. In addition, a number of unreported altered areas were identified. Field observations and X-ray diffraction (XRD) analysis of field samples confirmed the dominant mineral phases identified remotely. Results of LSU and MTMF were generally similar with overall accuracy of 82.9 and 90.24%, respectively. It is concluded that LSU and MTMF are suitable for sub-pixel mapping of alteration minerals and when the purpose is identification of particular targets, rather than all the elements in the scene, the MTMF algorithm could be proposed. [ABSTRACT FROM AUTHOR]

Abstract: The algorithms used in Arabic and medieval European mathematics for extracting cube roots are studied with respect to algebraic structure and use of external memory (dust board, table, paper). They can be separated into two distinct groups. One contains methods used in the eastern regions from the 11th century, closely connected to Chinese techniques, and very uniform in structure. The other group, showing much wider variation, contains early Indian methods and techniques developed in central and western parts of the Arabic areas and in Europe. This study supports hypotheses previously formulated by Luckey and Chemla on an early scientific connection between China and Persia. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach to improve the performance of neural network method to PVT oil properties prediction. The true value of PVT properties which is determined based on the accurate data is a challenge of the petroleum industry. The main goal of the following investigation would be the performance comparison of various back-propagation learning algorithms in neural network that could be applied for PVT prediction. Up to now, no procedure has been presented to determine the network structure for some complicated cases, therefore; design and production of neural network would be almost dependent on the user''s experience. To prevent this problem, neural network based recommended procedure in this study was applied to present the advantages. To show the performance of this procedure, several learning algorithms were investigated for comparison. One of the most common problems in neural network design is the topology and the parameter value accuracy that if those elements selection was correctly and optimally, the designer would achieve better results. Since, fluids of different regions have varying hydrocarbon properties, therefore, the empirical correlations in different hydrocarbon systems should be investigated to find their accuracies and limitations. In this study, an investigation of different empirical correlations along with the artificial neural networks in Iran oilfields has been presented. Then, the new model of artificial neural network for prediction of PVT oil properties in Iran crude oil presented. To test this new method, it was evaluated by collecting dataset from 23 different oilfields in Iran (south, central, western and continental shelf). In this study, two networks for prediction of bubble point pressure values (Pb) and the oil formation volume factor at bubble point (Bob) were designed. The parameters and topology of the optimum neural networks were determined and in order to consider the effect of these networks designing on results, their performances were compared with various empirical correlations. According to comparison between the obtained results, it shows that the improved method presented has better performance rather than empirical and current methods in neural network designing in petroleum applications for these predictions. [Copyright &y& Elsevier]

Traveler Salesman Problem is one of the most important and application problems in the combination optimization district that transportation usage allocate the most important place to itself among practical. Since the success of problem solution represent its power of usage into the different science and engineering variety of methods are suggested for it's solution. In this paper, we find out the shortest tour by solving the Traveler Salesman Problem. That is concurrent for 104 Iran selected points and we use Harmony Search algorithm to solve it. In order to survay the applicability of results, we optimize them by changing algorithm's parameters . This comparison shows a remakable priority in answer qualification wich are result from Harmony Search algorithm by changing parameters. [ABSTRACT FROM AUTHOR]

Background: Hepatitis B virus (HBV) infection is an important health problem worldwide with critical outcomes. The nucleoside analog lamivudine (LMV) is a potent inhibitor of HBV polymer­ase and impedes HBV replication in patients with chronic hepatitis B. Treatment with LMV for long periods causes the appearance and reproduction of drug-resistant strains, rising to more than 40% after 2 years and to over 50% and 70% after 3 and 4 years, respectively.Objectives: Artificial neural networks (ANNs) were used to make predictions with regard to re­sistance phenotypes using biochemical and biophysical features of the YMDD sequence.Patients and Methods: The study population comprised patients who were intended for surgery in various hospitals in Tehran-Iran. An ACRS-PCR method was performed to distinguish muta­tions in the YMDD motif of HBV polymerase. In the training and testing stages, these parameters were used to identify the most promising optimal network. The ideal values of RMSE and MAE are zero, and a value near zero indicates better performance. The selection was performed using statistical accuracy measures, such as root mean square error (RMSE), coefficient of determina­tion (R2), and mean absolute error (MAE). The main purpose of this paper was to develop a new method based on ANNs to simulate HBV drug resistance using the physiochemical properties of the YMDD motif and compare its results with multiple regression models. Results: The results of the MLP in the training stage were 0.8834, 0.07, and 0.09 and 0.8465, 0.160.04 in the testing stage; for the total data, the values were 0.8549, 0.115, and 0.065, respec­tively. The MLP model predicts lamivudine resistance in HBV better than the MLR model. Conclusions: The ANN model can be used as an alternative method of predicting the outcome of HBV therapy. In a case study, the proposed model showed vigorous clusterization of predicted and observed drug responses. The current study was designed to develop an algorithm for pre­dicting drug resistance using chemiophysical data with artificially created neural networks. To this end, an intelligent and multidisciplinary program should be developed on the basis of the information to be gained on the essentials of different applications by similar investigations. This program will help design expert neural network architectures for each application auto­matically. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with estimation of electricity demand of Iran based on economic indicators using Particle Swarm Optimization (PSO) Algorithm. The estimation is based on Gross Domestic Product (GDP), population, number of customers and average price electricity by developing two different estimation models: a linear model and a non-linear model. The proposed models are obtained based upon available actual data of 21years; since 1980–2000. Then the models obtained are used to estimate the electricity demand of the target years; for a period of time e.g. 2001–2006 and the results obtained are compared with the actual demand during this period. Furthermore, to validate the results obtained by PSO, genetic algorithm (GA) is applied to solve the problem. The results show that the PSO is a useful optimization tool for solving the problem using two developed models and can be used as an alternative solution to estimate the future electricity demand. [Copyright &y& Elsevier]

Land surface temperature (LST) is a significant parameter for many applications. Many studies have proposed various algorithms, such as the split-window method, for retrieving surface temperatures from two spectrally adjacent thermal infrared bands of satellite data. Each algorithm is developed for a limited study area and application. In this paper, as part of developing an optimal split-window method in the southeast of Tehran province, Iran, four commonly applied algorithms to retrieve the LST from AVHRR were compared. This study was carried out in a wheat farm site located in the Pakdasht Agricultural Region. Measurements of LST over the farm were made with a manual infrared radiometer at the time of NOAA overpass for 18 days of May to June 2004. These days were cloud free over the study area. A total of 18 NOAA images were acquired for the days that LST measurements were made. The temperatures derived by the different split-window algorithms were compared to ground truth measurements. The performance of the split window algorithms was checked with three statistical indices: root mean square error (RMSE), mean bias error (MBE) and coefficient of determination (R2). The results showed that the Ulivieri split-window algorithm produced the lowest value of RMSE and MBE (2.71 and 0.26 K, respectively) and its highest value of R2 (0.92) gave more accurate results than the other algorithms. [ABSTRACT FROM AUTHOR]

A common method for assessing the energy potential of hydropower systems in Iran is the sequential streamflow routing with control on the energy-yield reliability. The method results in developing a reliability-based simulation (RBS) model that is used to analyze single-reservoir hydropower systems. In most of the cascade hydropower systems in the country, a single-reservoir RBS model is usually employed in the design and operation of each of the systems reservoirs. This paper presents a multireservoir RBS model considering the integrated operation of the systems. The model employs the general algorithm of the single-reservoir RBS model; however, with a single-period optimization submodel in determining releases from reservoirs during the system simulation. The objective function of the submodels minimizes the sum of reservoir releases and/or maximizes the sum of reservoir storages in each of the time periods, subject to a reliability constraint on the system’s energy yield. The single and multireservoir RBS models are applied in the Khersan hydropower system located in Iran as a case study. The multireservoir RBS, compared to the single-reservoir model, results in 7.9% excess firm-energy yield in reliability of 90%. [ABSTRACT FROM AUTHOR]

Abstract: In the absence of either satellite ephemeris information or camera model, rational functions are introduced by many investigators as mathematical model for image to ground coordinate system transformation. The dependency of this method on many ground control points (GCPs), numerical complexity, particularly terms selection, can be regarded as the most known disadvantages of rational functions. This paper presents a mathematical solution to overcome these problems. Genetic algorithms are used as an intelligent method for optimum rational function terms selection. The results from an experimental test carried out over a test field in Iran are presented as utilizing an IKONOS Geo image. Different numbers of GCPs are fed through a variety of genetic algorithms (GAs) with different control parameter settings. Some initial constraints are introduced to make the process stable and fast. The residual errors at independent check points proved that sub-pixel accuracies can be achieved even when only seven and five GCPs are used. GAs could select rational function terms in such a way that numerical problems are avoided without the need to normalize image and ground coordinates. [Copyright &y& Elsevier]

Abstract: In this paper, the stochastic optimal control algorithm (OPTCON) is used to calculate optimal fiscal and monetary policies under fixed, flexible and crawling peg exchange rate regimes for the third five year Iranian development plan (2000–2004). The results of the study show that in the absent of active monetary policy instruments, the government expenditures are greater and the optimal tax revenues are lower than the proposed values in Iran third development plan. However, under a flexible exchange rate regime, the optimal values of government expenditures are lower and the optimal values of tax revenues are greater than that proposed in Iran’s third development plan. The study also shows that using optimal macroeconomic policies lead to lower fluctuations in major macroeconomic variables. The main conclusion seems to be is that only under the flexible exchange rate regime, the macroeconomic goals of the plan can be achieved, the flexible exchange rate regime being recommended as a policy instrument for Iran’s fourth five year development plan. [Copyright &y& Elsevier]

Selection of non-deposit sites is a challenging issue affecting the application of supervised algorithms for modeling mineral exploration targets. For this, equal number of deposit and non-deposit sites has been widely applied for training purposes. In this paper, we investigated the effect of changes in the number of non-deposit sites on the effectiveness of exploration targeting models while the number of deposit sites is constant. The results obtained demonstrated that exploration targeting models are affected by the ratio of non-deposit and deposit sites. Thus, balancing between the number of deposit and non-deposit sites is an efficient way to produce more-reliable exploration targets when supervised algorithms are applied for modeling. The idea of this research came from the fact that mineralization is a rare event, and therefore, in a region of interest number of non-deposit sites is much more than that of deposit events. To illustrate the procedure proposed, we used an exploration dataset of porphyry Cu mineralization in Chahargonbad area, SE Iran. A sequence application of self-organizing map and multilayer perceptron neural network algorithm was applied to better illustration of the changing effects of the number of non-deposit sites on the ensuing exploration targeting models. • Counting of deposit and non-deposit sites is discussed for training purpose. • The number affects the ensuing exploration targeting models. • Making a balance between the two types of training data yields significant results. • Self-organizing map clustering approach leads to define non-deposit sites. [ABSTRACT FROM AUTHOR]

In this paper optimal and cost-effective design of a photovoltaic/wind turbine/fuel cell (PV/WT/FC), the renewable energy system is proposed with the objective of lifespan cost of hybrid system (LSCS) minimisation and considering reliability constraint as a maximum of load interruption probability (LIP) as LIPmax to supply the demand of residential-commercial centres (RCC) located in Iran based on real data of irradiance and wind speed of this region. A new hybrid meta-heuristic algorithm named hybrid grey wolf optimiser-sine cosine algorithm (HGWOSCA) based on the exponential decreasing function (EDF) with high accuracy and speed of optimisation in achieving to the global solution is applied to determine the optimal size of system components. The superiority of the proposed HGWOSCA is compared with SCA, GWO and particle swarm optimisation (PSO) methods in designing of different combinations of the hybrid system and under varying reliability constraint. The results showed that the PV/WT/FC combination is the best combination in view of LSCS and LIPmax for supplying the RCC as a cost-effective and reliable combination. The superiority of the HGWOSCA in designing different combinations of the hybrid system is proved with fewer LSCS and LIP than the other methods with more convergence speed and accuracy. The results clearly showed that the use of hydrogen storage as a reserve power has well compensated the fluctuations in renewable sources production for meeting the RCC load to achieve optimal reliability. The results cleared that the cost of per kW of RCC supply for LIPmax = 1% and LIPmax = 5% is 0.853 and 0.764 $. The results indicated that increasing the failure rate of the components increases the system cost and weakens the reliability. In addition, the results showed that increased FC efficiency has reduced the level of reserve hydrogen, reduced the LSCS, and improved reliability. [ABSTRACT FROM AUTHOR]

• A multivariate modeling of primary geochemical haloes in a gold ore deposit was performed. • A new approach was used to provide anomaly model based on the MAF and U-spatial statistic methods. • Provides new target area for exploration under cover in the study area. An accurate separation of anomalous areas (values) in primary geochemical halos with heterogeneous spatial variations requires an appropriate algorithm that considers the complex patterns and multivariate relationships of the input geochemical data. There are several problems in multivariate modeling of geochemical compositional data, because such data present spurious values of spatial correlation between the variables. To address this problem, this paper proposes an improved U-spatial statistic algorithm for 3D multivariate anomaly modeling of the geochemical dataset with compositional nature at the Sari Gunay epithermal gold deposit, NW Iran. To perform 3D multivariate modeling, isometric logarithmic ratio (ilr), as an effective method to transform compositional data from the simplex to real space, and minimum/maximum autocorrelation factor (MAF), as a multivariate geostatistical method, were used in an integrated analysis. In comparison to the other separation techniques, this integrated approach has an advantage to control the heterogeneous spatial variations in the input data. In this approach, we improved the U-spatial statistic method by using the variogram tool theory. By using this approach, we identified the primary geochemical haloes associated with Au-As-Sb-Hg front halo elements, Cu-Ag-Pb near-ore halo elements and Ni-Co tail halo elements at the Sari Gunay gold depsoit. The results were validated by using contrast coefficient and exploratory index tools. By examining the 3D multivariate primary anomalous geochemical halos, zones of possible hidden epithermal gold and porphyry copper–gold mineralizations were recognized at this deposit for future exploration. [ABSTRACT FROM AUTHOR]

In this paper, we study the spatial distribution and temporal evolution of the postseismic deformation of the 2010–2011 Rigan earthquake sequence which occurred at the southern termination of the East Lut fault system, southeast Iran. One-year GPS measurements after the Rigan earthquake sequence reveals right-lateral postseismic displacement along the East Chahqanbar fault and left-lateral postseismic displacement along the South Chahqanbar fault. To investigate the deformation variations in time and space, InSAR time-series of COSMO-SkyMed images is applied using the Small Baseline Subset algorithm. The results confirm a clear cumulative postseismic signal increasing to 8 mm during the first five months following the first mainshock in the direction of the coseismic displacement. The cumulative postseismic displacements are well correlated with the cumulative number of the aftershocks and their associated moment release. Considering this correlation and the observation of a sharp discontinuity along the coseismic fault in the displacement map, it is concluded that the after-slip mechanism is responsible for the observed postseismic deformation in the study region. This study is the first observation of a short-term postseismic motion in eastern Iran through geodetic data in contrast with long-lasting postseismic displacements following the earthquakes that occurred around Lut block. Modeling of the postseismic displacement results in a distributed slip pattern with a maximum slip of 0.8 m on the fault plane responsible for the 2010 Rigan coseismic deformation. This indicates that the postseismic deformation on barriers remained unbroken during the mainshock. • We investigate the postseismic deformation of the Rigan earthquake sequence using GPS data and InSAR COSMO-SkyMed images. • We estimate the cumulative signal increasing to 8 mm following the first mainshock. • We observe a maximum postseismic slip of 0.8 m on the same fault plane responsible for the earthquake. • We observe short-term and deep afterslip postseismic motion for the Rigan earthquake sequence. • We find out that the observed postseismic motion was dominantly seismic. [ABSTRACT FROM AUTHOR]

The optimal allocation and sizing of distributed generation (DG) resources are important in installing these resources, to improve the technical parameters of the network, including the power losses, voltage profile, and short-circuit level, as well as to increase economic factors. In this paper, a new multi-criteria algorithm and objective function are proposed for the optimal sizing and allocation of renewable and non-renewable DG resources simultaneously. The proposed algorithm is implemented on 63/20 kV substations at 20 kV levels. In the proposed objective function, all important technical and economic factors as well as important constraints, such as penetration level of DGs and budget constraint, are considered in a way that all factors are assigned to monetary values. Moreover, a new mathematical formulation is introduced for the allocation of renewable DG resources to reduce run-time optimization. The genetic algorithm (GA) is employed in the proposed algorithm to minimize the objective function. For renewable DG resources, photovoltaic panels and wind turbines, and for non-renewable DG resources, gas turbines are considered. The 115 buses network of Bakhtar Regional Electric Company (BREC) in Iran is used to evaluate the performance of the proposed algorithm. The results demonstrate that the proposed algorithm improves technical factors efficiently and maximizes the profitability of the investment. [ABSTRACT FROM AUTHOR]

• A novel healthcare waste location-routing problem is developed with a new perspective in healthcare logistics networks. • A stochastic essence for the emission of contamination is considered. • The management of medical wastes in Coronavirus Disease 2019 is focused. • A real case study is modeled to manage the logistics of infectious and non-infectious healthcare wastes. • A Multi-Objective Water-Flow like Algorithm with novel operators is developed and compared with the others. This paper presents a novel healthcare waste location-routing problem by concentrating on a new perspective in healthcare logistics networks. In this problem, there are healthcare, treatment, and disposal centers. Locations of healthcare centers are known, however, it is required to select appropriate locations for treatment, recycling, and disposal centers. Healthcare wastes are divided into infectious and non-infectious wastes. Since a great volume of healthcare wastes are infectious, the emission of contamination can have obnoxious impacts on the environment. The proposed problem considers a stochastic essence for the emission of contamination which depends on the transferring times. In this respect, transferring times between healthcare and treatment centers have been considered as normal random variables. As transferring time increases, it is more likely for the contamination to spread. Having visited a treatment center, infectious wastes are sterilized and they will no longer be harmful to the environment. This research develops a bi-objective mixed-integer mathematical formulation to tackle this problem. The objectives of this model are minimization of total costs and emission of contamination, simultaneously. Complexity of the proposed problem led the researchers to another contribution. This study also develops a Multi-Objective Water-Flow like Algorithm (MOWFA), which is a meta -heuristic, to solve the problem. This algorithm uses a procedure based on the Analytical Hierarchy Process (AHP) to rank the non-dominated solutions in the archive set. By means of a developed mating operator, the MOWFA utilizes the best ranked solutions of the archive in order to obtain high quality offspring. Two neighborhood operators have been designed for the MOWFA as the local search methods. Extensive computational experiments have been conducted to evaluate the effectiveness of the MOWFA on several test problems compared with other meta -heuristics, namely the Multi-Objective Imperialist Competitive Algorithm (MOICA) and Multi-Objective Simulated Annealing (MOSA). These experiments also include a real healthcare waste logistic network in Iran. The computational experiments demonstrate that our proposed algorithm prevails these algorithms in terms of some well-known performance evaluation measures. [ABSTRACT FROM AUTHOR]